In many fields of technology, in particular in the clinical field, in automotive and automation engineering, in robotics and in criminology, increasing use is currently being made of applications in image processing, pattern recognition and content-based image retrieval. CAD algorithms such as, for example, noise suppression, contrast improvement, edge detection, segmentation and clustering algorithms, required for image preprocessing and which are used in combination with feature extraction, pattern recognition and volume rendering algorithms, serve in this case for finding, segmenting, grouping, identifying and/or classifying, highlighting and/or 3D visualization by means of imaging methods of image objects displayed in two-dimensional form. In the case of an application in medical technology, these image objects can be, for example, abnormal tissue structures in a patient's body region that is to be examined such as, for example, pulmonary nodules, pulmonary embolisms, kidney stones and/or gallstones, intestinal polyps, metastases of gastrointestinal tumors, etc. that were displayed graphically, together with the abdominal, thoracic and/or pelvic body tissue surrounding them, with the aid of imaging methods of radiology (for example by means of sonography, X-ray computed tomography and/or magnetic resonance tomography) in the form of axial (transverse), sagittalen (lateral) and/or coronal (frontal) 2D tomograms.
Since many of the pattern recognition applications normally used in the course of computer-aided diagnostic tools operate with a relatively high recognition accuracy, and can therefore return a substantial number of findings at each individual program invocation that must then, for example, be displayed graphically in two and/or three dimensions and be individually evaluated by a radiologist, the evaluation of these result data is extremely time consuming. In addition, not all result data returned per program invocation have a high diagnostic significance or are suitable for predicting a specific course of disease. Depending on the type of sickness, a radiologist requires a different number of findings for the purpose of reliably diagnosing a specific clinical picture and for making a reliable prediction of the likely further course of this disease. The radiologist has in this case to monitor all the findings that are automatically documented by the respective application in a report file or findings file, in order to detect and reliably exclude possibly wrongly diagnosed positive and/or negative findings.
Once such a pattern recognition application suitable for carrying out a computer-aided diagnosis has been started, it is normally necessary for the CAD algorithms required to carry out a 2D or 3D image rendering of recognized image objects and/or for carrying out image postprocessing to be called up and started manually. This means that after the pattern recognition application has output its findings, the radiologist must call up all the CAD algorithms required for graphically visualizing the corresponding image objects, for image cropping and image section enlargement, for varying the display perspective, the color tone, the color saturation, the brightness and/or the contrast of individual image objects etc. separately from one another via control commands to be input manually. This leads to interruptions in the normal operational cycle that, when added up, signify a not inconsiderable expenditure of time unacceptable for routine use of a computer-aided diagnostic tool.
Moreover, with the computer-aided diagnostic tools currently commercially available, the radiologist is constrained to check all findings displayed in graphic form individually, to evaluate them and document them in a report file or findings file. This mode of procedure is extremely time consuming and yields no sort of advantages with regard to the reliability of the diagnosis of a disease and to the reliability of the prediction of a course of disease to be expected.